A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies

This paper proposes a model that allows for nonlinear risk exposures of hedge funds to various risk factors. We introduce a flexible threshold regression model and develop a Bayesian approach for model selection and estimation of the thresholds and their unknown number. In particular, we present a computationally flexible Markov chain Monte Carlo stochastic search algorithm which identifies relevant risk factors and/or threshold values. Our analysis of several hedge fund returns reveals that different strategies exhibit nonlinear relations to different risk factors, and that the proposed threshold regression model improves our ability to evaluate hedge fund performance.     

Macroeconomic risk and hedge fund returns

This paper estimates hedge fund and mutual fund exposure to newly proposed measures of macroeconomic risk that are interpreted as measures of economic uncertainty. We find that the resulting uncertainty betas explain a significant proportion of the cross-sectional dispersion in hedge fund returns. However, the same is not true for mutual funds, for which there is no significant relationship. After controlling for a large set of fund characteristics and risk factors, the positive relation between uncertainty betas and future hedge fund returns remains economically and statistically significant. Hence, we argue that macroeconomic risk is a powerful determinant of cross-sectional differences in hedge fund returns.     

Higher risk,lower returns: What hedge fund investors really earn

The returns of hedge fund investors depend not only on the returns of the funds they hold but also on the timing and magnitude of their capital flows in and out of these funds. We use dollar-weighted returns (a form of Internal Rate of Return (IRR)) to assess the properties of actual investor returns on hedge funds and compare them to buy-and-hold fund returns. Our main finding is that annualized dollar-weighted returns are on the magnitude of 3% to 7% lower than corresponding buy-and-hold fund returns. Using factor models of risk and the estimated dollar-weighted performance gap, we find that the real alpha of hedge fund investors is close to zero. In absolute terms, dollar-weighted returns are reliably lower than the return on the Standard & Poor's (S&P) 500 index, and are only marginally higher than the risk-free rate as of the end of 2008. The combined impression from these results is that the return experience of hedge fund investors is much worse than previously thought.     

Dynamic hedge fund portfolio construction: A semi-parametric approach

In this article, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. We compare the standard mean–variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, which is based on extreme value theory, copula and Monte Carlo simulation. We compare the semi-parametric approach with the standard, non-parametric approach, used to compute CVaR, CDaR and Omega, and the benchmark parametric approach, based on both static and dynamic mean–variance optimization. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the parametric mean–variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.     

Systematic risk and the cross section of hedge fund returns

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.     

Capacity constraints,investor information,and hedge fund returns

To identify capacity constraints in hedge funds and simultaneously gauge how well-informed hedge fund investors are, we need measures of investor demand that do not affect deployed hedge fund assets. Using new data on investor interest from a secondary market for hedge funds, this paper verifies the existence of capacity constraints in hedge funds. There is more mixed evidence on the information available to hedge fund investors. Buy and sell indications arrive following fund outperformance. While buy indications have little incremental power to predict hedge fund performance over and above well-known forecasting variables, sell indications do somewhat better.     

An advanced perspective on the predictability in hedge fund returns

This paper advances the research on the predictability in hedge fund returns, using a broad set of risk factors within a variety of different prediction models. Accounting for the fact that returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive economic risk factors, we advocate a non-parametric backward elimination regression approach. The interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable in terms of the observed direction of impact. Thus, taking into account the specific characteristics of this asset class, we find strong evidence of its return predictability.     

Exploring uncharted territories of the hedge fund Industry: Empirical characteristics of mega hedge fund firms

This paper investigates mega hedge fund management companies that collectively manage over 50% of the industry's assets, incorporating previously unavailable data from those that do not report to commercial databases. We find similarities among mega firms that report performance to commercial databases compared with those that do not. We show that the largest divergences between the performance of reporting and nonreporting mega firms can be traced to differential exposure to credit markets. Thus, the performance of hard-to-observe mega firms can be inferred from observable data. This conclusion is robust to delisting bias and the presence of serially correlated returns.     

Payout yield,risk, and mispricing: A Bayesian analysis

We develop a simple parametric model in which hypotheses about predictability, mispricing, and the risk-return tradeoff can be evaluated simultaneously, while allowing for time variation in both risk and expected return. Most of the return predictability based on aggregate payout yield is unrelated to market risk. We consider a range of Bayesian prior beliefs about the risk-return tradeoff and the extent to which predictability is driven by mispricing. The impact of these beliefs on an investor's certainty-equivalent return when choosing between a market index and riskless T-bills is economically significant, in both ex ante and out-of-sample analyses.     

Predicting stock returns: A regime-switching combination approach and economic links

This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.     

The alpha and omega of fund of hedge fund added value

In spite of a somewhat disappointing performance throughout the crisis, investors are showing interest in hedge funds. Still, funds of hedge funds keep on experiencing outflows. Can this phenomenon be explained by the failure of fund of hedge fund managers to deliver on their promise to add value through active management, or is it symptomatic of a move toward greater disintermediation in the hedge fund industry? We introduce a return-based attribution model allowing for a full decomposition of fund of hedge fund performance. The results of our empirical study suggest that funds of hedge funds are funds of funds like others. Strategic allocation turns out to be a crucial step in the investment process, in that it not only adds value over the long-term, but most importantly, it brings resilience precisely when investors need it the most. Fund picking, on the other hand, turns out to be a double-edged sword.     

Do hedge funds deliver alpha? A Bayesian and bootstrap analysis

Using a robust bootstrap procedure, we find that top hedge fund performance cannot be explained by luck, and hedge fund performance persists at annual horizons. Moreover, we show that Bayesian measures, which help overcome the short-sample problem inherent in hedge fund returns, lead to superior performance predictability. Sorting on Bayesian alphas, relative to OLS alphas, yields a 5.5% per year increase in the alpha of the spread between the top and bottom hedge fund deciles. Our results are robust and relevant to investors as they are neither confined to small funds, nor driven by incubation bias, backfill bias, or serial correlation.     

Mutual fund flows,expected returns,and the real economy

This paper investigates the relation between mutual fund flows and the real economy. The findings of this paper support the theory that the positive co-movement of flows into equity funds and stock market returns is explained by a common response to macroeconomic news. Variables that predict the real economy as well as the equity premium – in particular dividend-price ratio, default spread, relative T-Bill rate and consumption-wealth ratio – are related to fund flows and can account for the correlation of flows and market returns. Furthermore, consistent with the information-response hypothesis, mutual fund flows are forward-looking and predict real economic activity.     

Hedge fund pricing and model uncertainty

This article uses Bayesian model averaging to study model uncertainty in hedge fund pricing. We show how to incorporate heteroscedasticity, thus, we develop a framework that jointly accounts for model uncertainty and heteroscedasticity. Relevant risk factors are identified and compared with those selected through standard model selection techniques. The analysis reveals that a model selection strategy that accounts for model uncertainty in hedge fund pricing regressions can be superior in estimation/inference. We explore potential impacts of our approach by analysing individual funds and show that they can be economically important.     

Investing in mutual funds when returns are predictable

This paper forms investment strategies in US domestic equity mutual funds, incorporating predictability in (i) manager skills, (ii) fund risk loadings, and (iii) benchmark returns. We find predictability in manager skills to be the dominant source of investment profitability—long-only strategies that incorporate such predictability outperform their Fama-French and momentum benchmarks by 2 to 4%/year by timing industries over the business cycle, and by an additional 3 to 6%/year by choosing funds that outperform their industry benchmarks. Our findings indicate that active management adds significant value, and that industries are important in locating outperforming mutual funds.     

The liquidity risk of liquid hedge funds

This paper evaluates hedge funds that grant favorable redemption terms to investors. Within this group of purportedly liquid funds, high net inflow funds subsequently outperform low net inflow funds by 4.79% per year after adjusting for risk. The return impact of fund flows is stronger when funds embrace liquidity risk, when market liquidity is low, and when funding liquidity, as measured by the Treasury-Eurodollar spread, aggregate hedge fund flows, and prime broker stock returns, is tight. In keeping with an agency explanation, funds with strong incentives to raise capital, low manager option deltas, and no manager capital co-invested are more likely to take on excessive liquidity risk. These results resonate with the theory of funding liquidity by Brunnermeier and Pedersen (2009).     

The risk in hedge fund strategies: Theory and evidence from long/short equity hedge funds

Theory suggests that long/short equity hedge funds' returns come from directional as well as spread bets on the stock market. Empirical analysis finds persistent net exposures to the spread between small vs large cap stocks in addition to the overall market. Together, these factors account for more than 80% of return variation. Additional factors are price momentum and market activity. Combining two major branches of hedge fund research, our model is the first that explicitly incorporates the effect of funding (stock loan) on alpha. Using a comprehensive dataset compiled from three major database sources, we find that among the three thousand plus hedge funds with similar style classification, less than 20% of long/short equity hedge funds delivered significant, persistent, stable positive non-factor related returns. Consistent with the predictions of the Berk and Green (2004) model we find alpha producing funds decays to “beta-only” over time. However, we do not find evidence of a negative effect of fund size on managers' ability to deliver alpha. Finally, we show that non-factor related returns, or alpha, are positively correlated to market activity and negatively correlated to aggregate short interest. In contrast, equity mutual funds and long-bias equity hedge funds have no significant, persistent, non-factor related return. Expressed differently, L/S equity hedge funds, as the name suggests, do benefit from shorting. Besides differences in risk taking behavior, this is a key feature distinguishing L/S funds from long-bias funds.     

Liquidity risk and stock returns around the world

The recent global financial crisis demonstrates that market liquidity is a prominent systematic risk globally. We find that local liquidity risk, in addition to the local market, value and size factors, demands a systematic premium across stocks in 11 developed markets. This local pricing premium is smaller in countries where the country-level corporate boards are more effective and where there are less insider trading activities. We also discover that global liquidity risk is a significant pricing factor across all developed country market portfolios after controlling for global market, value, and size factors. The contribution of this risk to the return on a country market portfolio is economically and statistically significant within and across regions.     

Hedge fund portfolio construction: A comparison of static and dynamic approaches

This article studies the impact of modeling time-varying covariances/correlations of hedge fund returns in terms of hedge fund portfolio construction and risk measurement. We use a variety of static and dynamic covariance/correlation prediction models and compare the optimized portfolios’ out-of-sample performance. We find that dynamic covariance/correlation models construct portfolios with lower risk and higher out-of-sample risk-adjusted realized return. The tail-risk of the constructed portfolios is also lower. Using a mean-conditional-value-at-risk framework we show that dynamic covariance/correlation models are also successful in constructing portfolios with minimum tail-risk.     

Mutual fund flows and investor returns: An empirical examination of fund investor timing ability

We examine the timing ability of mutual fund investors using cash flow data at the individual fund level. Over 1991–2004 equity fund investor timing decisions reduce fund investor average returns by 1.56% annually. Underperformance due to poor timing is greater in load funds and funds with relatively large risk-adjusted returns. In particular, the magnitude of investor underperformance due to poor timing largely offsets the risk-adjusted alpha gains offered by good-performing funds. Investors in both actively managed funds and index funds exhibit poor investment timing. We demonstrate that our empirical results are consistent with investor return-chasing behavior.